Sentiment analysis is a task of classifying documents into emotive categories, such as positive or negative. Corporations have used conventional sentiment analysis to track public opinion, employee attitude, and customer satisfaction with products of the corporations. Following general methodologies of information retrieval, there are two predominant methods for identifying sentiment or affect in documents and include text classification models and lexical approaches. Conventional classification models may utilize documents which are hand labeled for affect or sentiment, and a system may be trained using the labels. Some conventional classification models may utilize a relatively large amount of training data and may be relatively domain dependent. Some conventional lexical approaches may be susceptible to contextual variability of sentiment in plural domains or incomplete coverage of lexicons.
At least some aspects of the disclosure are directed towards improved apparatus and methods with respect to aspects of sentiment or affect analysis.